An Evaluation of the Psychometric Properties of the Montreal Cognitive Assessment Tool when Administered in a Memory Clinic At Groote Schuur Hospital, Cape Town, South Africa
Bibliographic record
Abstract
Worldwide, the population is aging, and the prevalence of neurocognitive disorders is expected to rise exponentially. Therefore, early detection of dementia is favorable for the patient and may even be of greater significance if disease-modifying treatments are discovered. The Montreal Cognitive Assessment (MoCA) is a reliable and valid cognitive screening tool but is sensitive to several sociodemographic factors, including language, culture, and quality of education. This underscores the need for cognitive screening scales validated in the culturally diverse South African setting. Aim. The purpose of this study was to investigate the utility of the MoCA as a brief cognitive screening tool in a specialized clinical South African sample. Methods. A retrospective medical folder review of 162 patients seen at Groote Schuur Hospital Memory Clinic for the first time between January 2014 and August 2021. Results. The median age of participants was 67 years (IQR 58-73). Most were females (63%, n =102), and had dementia (58%, n = 94); more than half (51%, n = 78) had at least 12 years of formal education. Older age and lower levels of education were associated with lower MoCA scores (p < 0.001). Conclusion. In a specialized South African clinical setting, the MoCA demonstrated good psychometric properties as a screening tool for evaluating different levels of cognitive impairment. However, to our knowledge, this is the first South African study to assess the factor structure of the MoCA in a clinical setting. More comprehensive and larger studies should evaluate the validity of our findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".